Dynamic Congestion Pricing for Ridesourcing Traffic: A Simulation Optimization Approach

Proceedings of Winter Simulation Conference, 2019

12 Pages Posted: 4 Dec 2019

See all articles by Qi Luo

Qi Luo

Clemson University, Department of Industrial Engineering

Zhiyuan Huang

Tongji University - School of Economics and Management

Henry Lam

Columbia University

Date Written: May 16, 2019

Abstract

Despite the documented benefits of ride-sourcing services, recent studies show that they can slow down traffic in the densest cities significantly. To implement congestion pricing policies upon those vehicles, regulators need to estimate how much their congestion effects are. This paper studies simulation-based approaches to address the two technical challenges arising from the representation of system dynamics and the optimization for congestion price mechanisms. To estimate the traffic conditions, we use a meta-model representation for traffic flow and a numerical method for data interpolation. To reduce the burden of replicating evaluation in stochastic optimization, we use a simulation optimization approach to compute the optimal congestion price. This data-driven approach can potentially be extended to solve large-scale congestion pricing problems with unobservable states.

Suggested Citation

Luo, Qi and Huang, Zhiyuan and Lam, Henry, Dynamic Congestion Pricing for Ridesourcing Traffic: A Simulation Optimization Approach (May 16, 2019). Proceedings of Winter Simulation Conference, 2019, Available at SSRN: https://ssrn.com/abstract=3488462 or http://dx.doi.org/10.2139/ssrn.3488462

Qi Luo (Contact Author)

Clemson University, Department of Industrial Engineering ( email )

227B Freeman Hall
Clemson, SC 29634
United States

Zhiyuan Huang

Tongji University - School of Economics and Management ( email )

Siping Road 1500
Shanghai, Shanghai 200092
China

Henry Lam

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

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